Perfect posterior simulation for mixture and hidden Markov models
Kasper K. Berthelsen, L.A. Breyer, Gareth O. Roberts · LMS Journal of Computation and Mathematics · 2010
Abstract In this paper we present an application of the read-once coupling from the past algorithm to problems in Bayesian inference for latent statistical models. We describe a method for perfect simulation from the posterior distribution of the unknown mixture weights in a mixture model. Our method is extended to a more general mixture problem, where unknown parameters exist for the mixture components, and to a hidden Markov model.